10 Quick Tips for Starting Your Big Data and Hadoop Journey
The power of data has become undeniable, especially since Big Data and Hadoop have been creating a lot of noise in the world of data. Many companies are just recently getting on the big data trend and learning more about how it can become an essential part of their company.
Big data shows a lot of potential for a number of companies. Between all the data and information, there are important trends that can help companies make better and more profitable decisions. With devices getting ample data from their users, there is now more data than ever. This is why the demand for data analysts has become important, with companies now looking to get more out of their data. A lot of developers are now taking their chance and are looking to learn Hadoop and BigData technologies.
According to the International Institute for Analytics, by 2020, businesses that are using data to make decisions will garner around $430 billion in productivity benefits over their competition who are not using data.
The benefits of using data for understanding business trends has become very important in today’s world. According to IDC analysts, “Total revenues from big data and business analytics will rise from $122 billion in 2015 to $187 billion in 2019.”
Importance of Big Data
In today’s world, when every device we touch generates data including our smartphones, the potential for companies to learn user wants and needs has become very important. Personalization plays a huge role, placing items where we want to see them. Ever notice the Recommended Section from Amazon, it uses user preferences and everything they’ve viewed on the website before to show the user a list of things that it believes the user might be interested in.
Using Big Data analytics, companies can accomplish important tasks such as:
- Understanding the root cause of failures, issues and defects in near-real time.
- Issuing coupons at the point-of-sale based on customer habits
- Recalculating risk portfolios
- Detecting fraudulent behavior
- Understanding the customer’s purchasing habits and making relevant suggestions
- Building a better relationship with the customer
These are just a few of the tasks that companies are capable of doing with big data.
Now, let’s get down to 10 quick tips on starting your Big Data and Hadoop journey.
1) Don’t Get Caught up in the Hype
Everything generates data, which means companies now have access to more data than ever. From sensors to smartphones, everything can be a way to accumulate data. However, one should remember not to get caught up in the hype of shiny technologies. Start with small amounts of data and learn the basic technologies first. It’s best to learn how to crawl, before you can start running and the same works with data. Go through the small data sets, go through tutorials for easiest technologies and then delve into more complicated technologies.
2) Data Isn’t Everything
Many companies become obsessed with data in the beginning, and start collecting every little bit of data that they can find. However, that isn’t useful. A lot of information is simply useless or doesn’t make an impact in the long run. Companies should instead focus on collecting the data they want or need to help them answer business questions.
3) Understand Your Business Case
Data Lakes are pretty and the idea that you can just dump all your data into one place until you need it for later may sound like a pretty awesome deal, but data lakes can become very confusing, very quickly. All the data in one place becomes difficult to sort later on. Instead, one should understand the type of data they need, i.e. the data that will give them valuable insights about their businesses. Data analysts should also remember that when you have larger amounts of data, the harder it becomes to sort them. So, try to create small sections of organized data that will be easier to sort using algorithms.
4) Learn From Your Failures And Focus On Success
Data Science doesn’t automatically come up with the right recipe for success, it requires a series of hits and misses before analysts and companies can understand how to exactly sort the data as well as how much of the data is useful. There are technologies such as Apache Spark that are focused on accelerating the process.
5) Learn New Technologies
There are a number of different technologies that are available for not only sorting big data, but also analyzing and organizing it. In addition to Hadoop, which is one of the most popular and best big data technologies, there are also other technologies such as Hive, Apache Spark, Presto, Tableau, and so many more. It is often a good idea to stay on top of all these different technologies.
6) Learn to see the Big Picture
Data analysts are responsible for thinking outside the box. They are responsible for building new apps based on the data that they collect, process and analyze. With the amounts of data that they are faced with, they can get caught up in the little details, which can result in a creative block. So, sometimes looking at the big picture can help.
7) Make Sure Your Model Works
You may have the best predictive model, but unless it can be operationalized in the real world and have a positive impact, then it really has no value to the organization and is a waste of time and resources. A predictive model is only useful if it works, and while it might look good on paper, it might not translate well in reality. So, ensure that your model checks all the boxes.
8) Less Can Be More
When it comes to big data, analysts are often tempted to try and experiment with everything that they can get their hands on. When you are collecting so much data, and the algorithms are offering decent results, you might be tempted to apply analysis to all aspects of your business. However, you should focus on a few aspects instead of trying to push big data on everything.
9) Focus on Teams, Not Unicorns
Data scientists that are well-versed in all three aspects – statistics, technology, and business – are often known as ‘unicorns’. This is because they are exceptionally rare. Companies often try to find analysts that can fit all these criteria and they should not. Big Data teams are more efficient and can offer better results than these ‘unicorns’.
10) Learn How To Visualize Your Data
Hadoop is a great software to crunch the numbers, and while analysts often understand the numbers – laymen don’t. These laymen are responsible for making the big decisions. This is why it is important for analysts to also learn about data visualization software such as Tableau, or D3.JS that can help you visualize your data to make it easier to present.
The importance of big data has resulted in a lot of people turning toward big data and Hadoop for building careers. Since Big Data and Hadoop has a lot of potential, it is a great career if you enjoy making sense of numbers. There are many great Hadoop tutorials online to help you master this amazing technology.